Human Event-related Potential Research Articles

Neural evidence for referential understanding of object words in dogs

Using words to refer to objects in the environment is a core feature of the human language faculty. Referential understanding assumes the formation of mental representations of these words.1,2 Such understanding of object words has not yet been demonstrated as a general capacity in any non-human species,3 despite multiple behavior-based case reports.4,5,6,7,8,9,10 In human event-related potential (ERP) studies, object word knowledge is typically tested using the semantic violation paradigm, where words are presented either with their referent (match) or another object (mismatch).11,12 Such mismatch elicits an N400 effect, a well-established neural correlate of semantic processing.12,13 Reports of preverbal infant N400 evoked by semantic violations14 assert the use of this paradigm to probe mental representations of object words in nonverbal populations. Here, measuring dogs' (Canis familiaris) ERPs to objects primed with matching or mismatching object words, we found a mismatch effect at a frontal electrode, with a latency (206-606ms) comparable to the human N400. A greater difference for words that dogs knew better, according to owner reports, further supported a semantic interpretation of this effect. Semantic expectations emerged irrespective of vocabulary size, demonstrating the prevalence of referential understanding in dogs. These results provide the first neural evidence for object word knowledge in a non-human animal. VIDEO ABSTRACT.

Explaining the Sentence Superiority Effect and N400s Elicited by Words and Short Sentences with OB1-Reader.

Research into reading has benefitted from the emergence of powerful computational models that account for reading behavior at different levels. Such models become more powerful when the underlying anatomy, architecture or 'physiology' can be linked to the behavior of interest. OB1-reader is a reading model that simulates the processes underlying reading in the human brain. Previous studies showed that OB1-reader can account for various phenomena in the word recognition and text reading literatures. Here we aim to extend OB1's scope, by simulating behavioral performance and evoked EEG activity for two experimental word-recognition tasks: a flanker task in which unrelated flankers generated less accurate responses combined with a larger N400, and a sentence reading task in which words were recognized more accurately at central positions and within intact sentences, than at peripheral positions and in scrambled sentences. OB1 simulated several behavioral findings in both paradigms, including the so-called sentence superiority effect. Moreover, virtual event-related potentials (ERPs) generated from node activity in OB1 were compared to human ERPs. More lexical activity in OB1 predicted the size of the N400 component of human readers in both experiments, but not the N250.

Pathologists aren't pigeons: exploring the neural basis of visual recognition and perceptual expertise in pathology.

Visual (perceptual) reasoning is a critical skill in many medical specialties, including pathology, diagnostic imaging, and dermatology. However, in an ever-compressed medical curriculum, learning and practicing this skill can be challenging. Previous studies (including work with pigeons) have suggested that using reward-feedback-based activities, novices can gain expert levels of visual diagnostic accuracy in shortened training times. But is this level of diagnostic accuracy a result of image recognition (categorization) or is it the acquisition of diagnostic expertise? To answer this, the authors measured electroencephalographic data (EEG) and two components of the human event-related brain potential (reward positivity and N170) to explore the nature of visual expertise in a novice-expert study in pathology visual diagnosis. It was found that the amplitude of the reward positivity decreased with learning in novices (suggesting a decrease in reliance on feedback, as in other studies). However, this signal remained significantly different from the experts whose reward positivity signal did not change over the course of the experiment. There were no changes in the amplitude of the N170 (a reported neural marker of visual expertise) in novices over time. Novice N170 signals remained statistically and significantly lower in amplitude compared to experts throughout task performance. These data suggest that, while novices gained the ability to recognize (categorize) pathologies through reinforcement learning as quantified by the change in reward positivity, increased accuracy, and decreased time for responses, there was little change in the neural marker associated with visual expertise (N170). This is consistent with the multi-dimensional and complex nature of visual expertise and provides insight into future training programs for novices to bridge the expertise gap.

How low can you go? Measuring human event-related brain potentials from a two-channel EEG system

Over the past ten years, there has been a rapid increase in the availability and use of mobile electroencephalography (mEEG) in research. Indeed, researchers using mEEG have recorded EEG and event-related brain potentials in a wide range of environments - for example, while walking (Debener et al., 2012), riding a bike (Scanlon et al., 2020), or even in a shopping mall (Krigolson et al., 2021). However, given that low-cost, ease-of-use, and setup speed provide the primary advantages of an mEEG system over large array traditional EEG systems, an important and unresolved question is just how many electrodes does an mEEG system need to collect research-quality EEG data? Here, we tested whether or not a two-channel forehead-mounted mEEG system – the “Patch” – could measure event-related brain potentials within their established amplitude and latency characteristics (Luck, 2014). In the present study, participants performed a visual oddball task while we recorded EEG data from the Patch. Our results demonstrated that we could capture and quantify the N200 and P300 event-related brain potential components using a minimal electrode array forehead-mounted EEG system. Our data further support the idea that mEEG can be used for quick and rapid EEG-based assessments, such as measuring the impact of concussions on the sports field (Fickling et al., 2021) or assessing the impact of stroke severity in a hospital (Wilkinson et al., 2020).

A Guided Tutorial on Modelling Human Event-Related Potentials with Recurrent Neural Networks

In cognitive neuroscience research, computational models of event-related potentials (ERP) can provide a means of developing explanatory hypotheses for the observed waveforms. However, researchers trained in cognitive neurosciences may face technical challenges in implementing these models. This paper provides a tutorial on developing recurrent neural network (RNN) models of ERP waveforms in order to facilitate broader use of computational models in ERP research. To exemplify the RNN model usage, the P3 component evoked by target and non-target visual events, measured at channel Pz, is examined. Input representations of experimental events and corresponding ERP labels are used to optimize the RNN in a supervised learning paradigm. Linking one input representation with multiple ERP waveform labels, then optimizing the RNN to minimize mean-squared-error loss, causes the RNN output to approximate the grand-average ERP waveform. Behavior of the RNN can then be evaluated as a model of the computational principles underlying ERP generation. Aside from fitting such a model, the current tutorial will also demonstrate how to classify hidden units of the RNN by their temporal responses and characterize them using principal component analysis. Statistical hypothesis testing can also be applied to these data. This paper focuses on presenting the modelling approach and subsequent analysis of model outputs in a how-to format, using publicly available data and shared code. While relatively less emphasis is placed on specific interpretations of P3 response generation, the results initiate some interesting discussion points.

Recurrent Neural Network Model of Human Event-related Potentials in Response to Intensity Oddball Stimulation

The mismatch negativity (MMN) component of the human event-related potential (ERP) is frequently interpreted as a sensory prediction-error signal. However, there is ambiguity concerning the neurophysiology underlying hypothetical prediction and prediction-error signalling components, and whether these can be dissociated from overlapping obligatory components of the ERP that are sensitive to physical properties of sounds. In the present study, a hierarchical recurrent neural network (RNN) was fitted to ERP data from 38 subjects. After training the model to reproduce ERP waveforms evoked by 80 dB standard and 70 dB deviant stimuli, it was used to simulate a response to 90 dB deviant stimuli. Internal states of the RNN effectively combined to generate synthetic ERPs, where individual hidden units are loosely analogous to population-level sources. Model behaviour was characterised using principal component analysis of stimulus condition, layer, and individual unit responses. Hidden units were categorised according to their temporal response fields, and statistically significant differences among stimulus conditions were observed for amplitudes of units peaking in the 0–75 ms (P50), 75–125 ms (N1), and 250–400 ms (N3) latency ranges, surprisingly not including the measurement window of MMN. The model demonstrated opposite polarity changes in MMN amplitude produced by falling (70 dB) and rising (90 dB) intensity deviant stimuli, consistent with loudness dependence of sensory ERP components. This modelling study suggests that loudness dependence is a principal driver of intensity MMN, and future studies ought to clarify the distinction between loudness dependence, adaptation and prediction-error signalling.

A Review on P300 in Obsessive-Compulsive Disorder.

Neuropsychological studies indicate the presence of cognitive changes in patients with obsessive-compulsive disorder (OCD). Indeed, OCD may be included among the dysfunctions of the frontal lobes and their connections with the limbic system, associative cortex, and basal ganglia. P300 is a positive component of the human event-related potential (ERP); it is associated with processes of encoding, identification, and categorization constituting, as a whole, the superior cortical function of information processing. Thus, P300 explores several areas that are implicated in OCD pathophysiology. Our aim is to review all relevant studies on the P300 component of the human ERP in order to recognize any significant central nervous system (CNS) correlate of cognitive dysfunction in OCD. A PubMed-based literature search resulted in 35 articles assessing P300 in OCD and reporting neurophysiological correlates of response inhibition, cortical hyperarousal, and over-focused attention. A decreased P300 amplitude was reported in both adult and pediatric patients, with a trend toward normalization after pharmacological treatment. Source localization studies disclosed an association between P300 abnormalities and the functioning of brain regions involved in the pathophysiology of OCD. Moreover, studies converge on the evidence of neurophysiological dysfunction in the frontal areas with impairment of the normal inhibitory processes in OCD. At least some of these electrophysiological correlates might reflect the obsessive thoughts and compulsions that characterize this disorder. These findings may also support cognitive-behavioral therapy (CBT) approaches on over-focused attention and inflexibility of compulsive behaviors, which should be associated to pharmacological treatment in these patients.

P300 and decision-making in neuromarketing

Neuromarketing provides insights into consumers' decision-making that traditional marketing test methods cannot offer. The foundation in the process of decision-making is P300. Thus, the P300 wave is a potential Event-Related Component (ERP) used to measure consumers' decision-making process. The P300 wave represents a positive transition in human event-related potential. Therefore, the P300 is determined by measuring the amplitude and latency of the consumers. A higher P300 amplitude indicates greater confidence in the decision-making process, while a longer P300 latency indicates lower attentiveness. Thus, P300 in neuroscience, which investigates customers' responses in-depth, cannot be accomplished by typical marketing methods. For many years, P300 components such as attitudes, preferences, and information-based decision-making have been examined extensively in marketing-related research. However, a review of an ERP in neuromarketing method is fewer reported. This mini review describes some analysis on P300 and decision-making by several researchers.

The ERP, frequency, and time-frequency correlates of feedback processing: Insights from a large sample study.

Human learning, at least in part, appears to be dependent on the evaluation of how outcomes of our actions align with our expectations. Over the past 23years, electroencephalography (EEG) has been used to probe the neural signatures of feedback processing. Seminal work demonstrated a difference in the human event-related potential (ERP) dependent on whether people were processing correct or incorrect feedback. Since then, these feedback evoked ERPs have been associated with reinforcement learning and conflict monitoring, tied to subsequent behavioral adaptations, and shown to be sensitive to a wide range of factors (e.g., Parkinson's disease). Recently, research has turned to frequency decomposition techniques to examine how changes in the EEG power spectra are related to underlying learning mechanisms. Although the literature on the neural correlates of feedback processing is vast, there are still methodological discrepancies and differences in results across studies. Here, we provide reference results and an investigation of methodological considerations for the ERP (reward positivity) and frequency (delta and theta power) correlates of feedback evaluation with a large sample size. Specifically, participants (n=500) performed a two-armed bandit task while we recorded EEG. Our findings provide key information about the data characteristics and relationships that exist between the neural signatures of feedback evaluation. Additionally, we conclude with selected methodological recommendations for standardization of future research. All data and scripts are freely provided to facilitate open science.

Learning to predict: Neuronal signatures of auditory expectancy in human event-related potentials

Learning to anticipate future states of the world based on statistical regularities in the environment is a key component of perception and is vital for the survival of many organisms. Such statistical learning and prediction are crucial for acquiring language and music appreciation. Importantly, learned expectations can be implicitly derived from exposure to sensory input, without requiring explicit information regarding contingencies in the environment. Whereas many previous studies of statistical learning have demonstrated larger neuronal responses to unexpected versus expected stimuli, the neuronal bases of the expectations themselves remain poorly understood. Here we examined behavioral and neuronal signatures of learned expectancy via human scalp-recorded event-related brain potentials (ERPs). Participants were instructed to listen to a series of sounds and press a response button as quickly as possible upon hearing a target noise burst, which was either reliably or unreliably preceded by one of three pure tones in low-, mid-, and high-frequency ranges. Participants were not informed about the statistical contingencies between the preceding tone ‘cues’ and the target. Over the course of a stimulus block, participants responded more rapidly to reliably cued targets. This behavioral index of learned expectancy was paralleled by a negative ERP deflection, designated as a neuronal contingency response (CR), which occurred immediately prior to the onset of the target. The amplitude and latency of the CR were systematically modulated by the strength of the predictive relationship between the cue and the target. Re-averaging ERPs with respect to the latency of behavioral responses revealed no consistent relationship between the CR and the motor response, suggesting that the CR represents a neuronal signature of learned expectancy or anticipatory attention. Our results demonstrate that statistical regularities in an auditory input stream can be implicitly learned and exploited to influence behavior. Furthermore, we uncover a potential ‘prediction signal’ that reflects this fundamental learning process.

ERP CORE: An open resource for human event-related potential research

Event-related potentials (ERPs) are noninvasive measures of human brain activity that index a range of sensory, cognitive, affective, and motor processes. Despite their broad application across basic and clinical research, there is little standardization of ERP paradigms and analysis protocols across studies. To address this, we created ERP CORE (Compendium of Open Resources and Experiments), a set of optimized paradigms, experiment control scripts, data processing pipelines, and sample data (N = 40 neurotypical young adults) for seven widely used ERP components: N170, mismatch negativity (MMN), N2pc, N400, P3, lateralized readiness potential (LRP), and error-related negativity (ERN). This resource makes it possible for researchers to 1) employ standardized ERP paradigms in their research, 2) apply carefully designed analysis pipelines and use a priori selected parameters for data processing, 3) rigorously assess the quality of their data, and 4) test new analytic techniques with standardized data from a wide range of paradigms.

Global statistical inference for the difference between two regression mean curves with covariates possibly partially missing

In two sample problems it is of interest to examine the difference between the two regression curves or to detect whether certain functions are adequate to describe the overall trend of the difference. In this paper, we propose a simultaneous confidence band (SCB) as a global inference method with asymptotically correct coverage probabilities for the difference curve based on the weighted local linear kernel regression estimates in each sample. Our procedure allows for random designs, different sample sizes, heteroscedastic errors, and especially missing covariates. Simulation studies are conducted to investigate the finite sample properties of the new SCB which support our asymptotic theory. The proposed SCB is used to analyze two data sets, one of which is concerned with human event-related potentials data which are fully observed and the other is concerned with the Canada 2010/2011 youth student survey data with partially missing covariates, leading to a number of discoveries.

Intra- and Inter-Rater Reliability of Manual Feature Extraction Methods in Movement Related Cortical Potential Analysis.

Event related potentials (ERPs) provide insight into the neural activity generated in response to motor, sensory and cognitive processes. Despite the increasing use of ERP data in clinical research little is known about the reliability of human manual ERP labelling methods. Intra-rater and inter-rater reliability were evaluated in five electroencephalography (EEG) experts who labelled the peak negativity of averaged movement related cortical potentials (MRCPs) derived from thirty datasets. Each dataset contained 50 MRCP epochs from healthy people performing cued voluntary or imagined movement, or people with stroke performing cued voluntary movement. Reliability was assessed using the intraclass correlation coefficient and standard error of measurement. Excellent intra- and inter-rater reliability was demonstrated in the voluntary movement conditions in healthy people and people with stroke. In comparison reliability in the imagined condition was low to moderate. Post-hoc secondary epoch analysis revealed that the morphology of the signal contributed to the consistency of epoch inclusion; potentially explaining the differences in reliability seen across conditions. Findings from this study may inform future research focused on developing automated labelling methods for ERP feature extraction and call to the wider community of researchers interested in utilizing ERPs as a measure of neurophysiological change or in the delivery of EEG-driven interventions.

Evoked Frontal and Parietal Field Potential Signatures of Target Detection and Response Inhibition in Rats Performing an Equiprobable Auditory Go/No-Go Task.

To characterize the rat as a potential model of frontal-parietal auditory processing during sustained attention, target detection, and response inhibition, we recorded field potentials (FPs) at multiple sites in medial-dorsal frontal and posterior parietal cortex simultaneously while rats performed an equiprobable auditory go/no-go discrimination task. Event-related potentials (ERPs) were calculated by averaging tone-triggered FPs across hit, miss, false alarm (FA), and correct rejection (CR) trials separately for each recording session, and five peak amplitudes (termed N1, P2, N2, P3E, and P3L) were extracted from the individual-session ERPs. Comparing peak amplitudes across different trials types indicated a statistically significant amplification of the P2 peak on hit trials that accompanies detection of the target tone prior to the behavioral go response. This result appears analogous to human ERP phenomena during auditory target discrimination. Conversely, the rat P3 responses were not associated with target detection as in the human ERP literature. Likewise, we did not observe the "no-go N2" or "no-go P3" responses reported in the human literature in association with response inhibition, which might reflect differences in task context or a difference in auditory processing between rats and humans. We also present analyses of stimulus-induced spectral power and interarea coherence to characterize oscillatory synchronization which may contribute to ERPs, and discuss possible error-related processing at the N2, P3E, and P3L peaks.

Event-related brain potentials to self-triggered tones: Impact of action type and impulsivity traits

Human event-related potentials (ERPs) have previously been observed to be attenuated for self-triggered sounds and amplified for deviant auditory stimuli. These auditory ERP modulations have been proposed to reflect internal predictions about the sensory consequences of our actions and more generally about our sensory context. The present exploratory ERP study (1) compared the processing of self-triggered tones by either intention-based or stimulus-driven actions, and (2) studied the impact of impulsivity traits on the prediction of action-effects. Our results are consistent with an early distinction, before N1, between tones triggered by intention-based actions and tones triggered by stimulus-driven actions. Moreover, we observed an enhanced N2b for deviant stimuli triggered by intention-based actions only. In addition, N2b modulations were correlated with impulsiveness scores. Altogether our results suggest that action-effect prediction is stronger in intention-based actions and impaired in impulsive participants but replication studies are needed to corroborate the reported findings.

Early cortical processing of vection-inducing visual stimulation as measured by event-related brain potentials (ERP)

Visual motion stimuli can induce the perception of self-motion in stationary observers (known as vection). In the present study, we investigated the sensory processing underlying vection by measuring the human event-related brain potentials (ERPs) elicited by the movement onset of a visual stimulus. We presented participants a visual stimulus consisting of alternating black-and-white vertical bars that moved in horizontal direction, creating the sensation of vection. The stimulus was presented on a screen that was divided into a central and a surrounding peripheral visual area. Both areas moved independently from each other, resulting in four different movement patterns: the peripheral and the central stimulus moved in the same or opposite direction, or one of the two visual areas of the stimulus moved while the other remained stable. In addition, two different stimulus types varying with respect to the bars’ width (narrow vs. wide) were used. Participants were presented with these stimuli in two phases of the experiment: During EEG-recording, only short phases (2.5–3.5 ms) of visual motion were applied. In a separate rating phase, visual motion was presented for 45 s. In this phase, vection onset, intensity, and duration were verbally recorded. Overall, the visual stimulation generated vection with different intensities (i.e., weakest vection with sole central stimulus movement). Stimulus type did not affect vection. In the ERPs, stimulus onset elicited parieto-occipital P2 and N2 components. The amplitudes of the ERP components differed significantly between the four movement patterns (irrespective of stimulus type), however, they did not fully align with the subjective vection ratings. Since the ERPs are associated with early sensory processing preceding vection, we argue that the ERPs mirror the contribution of sensory cortical processes to vection rather than the subjective sensation of vection per se.

Electrophysiological responses of medial prefrontal cortex to feedback at different levels of hierarchy

Recent advances in computational reinforcement learning suggest that humans and animals can learn from different types of reinforcers in a hierarchically organised fashion. According to this theoretical framework, while humans learn to coordinate subroutines based on external reinforcers such as food rewards, simple actions within those subroutines are reinforced by an internal reinforcer called a pseudo-reward. Although the neural mechanisms underlying these processes are unknown, recent empirical evidence suggests that the medial prefrontal cortex (MPFC) is involved. To elucidate this issue, we measured a component of the human event-related brain potential, called the reward positivity, that is said to reflect a reward prediction error signal generated in the MPFC. Using a task paradigm involving reinforcers at two levels of hierarchy, we show that reward positivity amplitude is sensitive to the valence of low-level pseudo-rewards but, contrary to our expectation, is not modulated by high-level rewards. Further, reward positivity amplitude to low-level feedback is modulated by the goals of the higher level. These results, which were further replicated in a control experiment, suggest that the MPFC is involved in the processing of rewards at multiple levels of hierarchy.

The oddball effect on P3 disappears when feature relevance or feature-response mappings are unknown.

The P3b component of human event-related EEG potentials is larger with rare than frequent task-relevant stimuli. In a previous study, this oddball effect was much reduced when stimulus-response (S-R) mappings were still undefined at stimulus presentation (being later provided by response prompts). This reduction may reflect P3b's dependence on transmitted information which might be any relevant information (informational value hypothesis) or, more specifically, information about how to respond (S-R link hypothesis). To distinguish between these two hypotheses and clarify their differences from classical stimulus evaluation hypothesis, we added a second dimension by presenting colored letters, with both colors and letters varying between a rare and a frequent alternative. Response prompts, presented half a second later, were, in different blocks, constant or variable across trials with respect to S-R mapping and with respect to the relevant dimension (color or letter). With partial information, when only one of these two factors is known at stimulus presentation (by being constant across trials), the hypotheses differ in their predictions. The oddball effect will be abolished according to S-R link hypothesis because knowledge of both factors is needed to determine the response, but will only be reduced according to informational value hypothesis and be fully maintained according to stimulus evaluation hypothesis. In fact, oddball effects only occurred with knowledge of both factors, i.e., if both the relevant dimension and its mapping to responses were constant across trials. These results confirm the preeminent role of knowledge about responses for eliciting P3.

Identification of TAAR5 Agonist Activity of Alpha-NETA and Its Effect on Mismatch Negativity Amplitude in Awake Rats.

Mismatch negativity (MMN) is a well-defined component of human event-related potentials that reflects the pre-attentive, stimulus-discrimination process and is associated with involuntary switching of attention. MMN-like responses detected in animal models provide an opportunity to investigate the neural mechanisms of this process that involves several neurotransmitter and neuromodulator systems. Trace amines are believed to play a significant role in neuromodulation of synaptic transmission. The present study aimed to determine the role of trace amine-associated receptor 5 (TAAR5) in the MMN-like response in rats. First, using a bioluminescence resonance energy transfer (BRET) cAMP biosensor, we performed unbiased screening of TAAR5 ligands from a commercially available compound library (661 compounds) and identified 2-(alpha-naphthoyl)ethyltrimethylammonium iodide (alpha-NETA) as a potent (EC50 150nM) TAAR5 agonist. Then, we recorded auditory event-related potentials during an oddball paradigm in awake freely moving rats that were intraperitoneally injected with a vehicle or two doses of the putative TAAR5 agonist alpha-NETA. The MMN-like response was increased by alpha-NETA 3mg/kg dose, but not by 1mg/kg dose or 0.9% saline solution. These results suggest that the MMN-like response in rats may be modulated, at least in part, through TAAR5-dependent processes.

Learning what matters: A neural explanation for the sparsity bias

The visual environment is filled with complex, multi-dimensional objects that vary in their value to an observer's current goals. When faced with multi-dimensional stimuli, humans may rely on biases to learn to select those objects that are most valuable to the task at hand. Here, we show that decision making in a complex task is guided by the sparsity bias: the focusing of attention on a subset of available features. Participants completed a gambling task in which they selected complex stimuli that varied randomly along three dimensions: shape, color, and texture. Each dimension comprised three features (e.g., color: red, green, yellow). Only one dimension was relevant in each block (e.g., color), and a randomly-chosen value ranking determined outcome probabilities (e.g., green > yellow > red). Participants were faster to respond to infrequent probe stimuli that appeared unexpectedly within stimuli that possessed a more valuable feature than to probes appearing within stimuli possessing a less valuable feature. Event-related brain potentials recorded during the task provided a neurophysiological explanation for sparsity as a learning-dependent increase in optimal attentional performance (as measured by the N2pc component of the human event-related potential) and a concomitant learning-dependent decrease in prediction errors (as measured by the feedback-elicited reward positivity). Together, our results suggest that the sparsity bias guides human reinforcement learning in complex environments.